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Module 03: Mocking and Test Doubles

← Unit Testing | Topic Home | Next → Integration Testing

Status Difficulty Time

Mocks vs. stubs vs. fakes vs. spies vs. dummies — the five test double types; when to mock; the over-mocking trap; Python's unittest.mock and pytest-mock; Jest mocks.

Overview

This module is a stub. Content will be added in a future iteration.

Topics covered in this module:

  • Gerard Meszaros's five test double types: dummy, stub, fake, spy, mock — precise definitions and when to use each
  • Python's unittest.mock in depth — MagicMock, patch, patch.object, side_effect, return_value
  • pytest-mock — cleaner mock API via the mocker fixture
  • Jest mocks — jest.fn(), jest.mock(), jest.spyOn(), mockReturnValue, mockImplementation
  • The over-mocking trap — when mocking makes tests brittle and meaningless
  • Designing for testability — dependency injection patterns that make mocking easy
  • Testing with time — freezing time in Python (freezegun) and JavaScript (jest.useFakeTimers)
  • Testing randomness — seeding random number generators in tests

Prerequisites

  • Module 02: Unit Testing in this topic
  • Basic understanding of Python modules and JavaScript modules

Objectives

By the end of this module, you will be able to:

  1. Identify the correct test double type for any given scenario
  2. Use Python's unittest.mock.patch to intercept and control dependencies
  3. Use Jest's mock functions and module mocking to isolate JavaScript code
  4. Recognize over-mocking and refactor tests that are too tightly coupled to implementation
  5. Design code with dependency injection to make mocking natural
  6. Mock time and randomness to make tests deterministic
  • [[qa-testing/modules/02_unit-testing]] — Unit testing concepts that this module extends
  • [[qa-testing/modules/04_integration-testing]] — Integration tests deliberately avoid mocking; understanding the contrast is important
  • [[qa-testing/glossary#test-double]] — Formal definition of test doubles